server.py

from flask import *
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

app=Flask('__name__')

url="https://raw.githubusercontent.com/anitabudhiraja/MachineLearning/main/iris.csv"
df=pd.read_csv(url)
np1=df.values
X=np1[:,0:4]
Y=np1[:,4]
validation_size=.20
seed=42
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=validation_size,random_state=seed)
# creating model instance
kclf_final=KNeighborsClassifier(n_neighbors=13)
kclf_final.fit(X_train,Y_train)
# predictions_f=kclf_final.predict(X_test)
# print(accuracy_score(predictions_f,Y_test))
# using cv=kfold

@app.route('/')
def home():
  return render_template("base.html")

@app.route('/model')
def model():
  return render_template('model.html')
@app.route('/model_connect',methods=['POST'])
def model_connect():
  Sepal_L=float(request.form['sepall'])
  Sepal_W=float(request.form['sepalw'])
  Petal_L=float(request.form['petall'])
  Petal_W=float(request.form['petalw'])
  predict=kclf_final.predict([[Sepal_L,Sepal_W,Petal_L,Petal_W]])
  return render_template('model.html',predictions=predict[0])
  

if __name__=='__main__':
   app.run()